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Objective:To evaluate spectral CT metal artifacts reduction (MAR) technique in reducing metal artifacts of spinal implants in a phantom.Methods:Ovine spines were chosen as anthropomorphic phantom. The phantom including the pedicle screws, 3D-printed vertebral body (VB) and mesh cage were examined using spectral CT. Postoperative CT images were reconstructed at 70—140 keV with 10 keV interval of MAR and non-MAR. Artifact index (AI) and signal-to-noise ratio (SNR) were evaluated by CT and SD values in ROIs around the implants. Visibility of bony structures, the artifacts of pedicle screw, 3D-printed VB and mesh cage were subjectively evaluated. Plotting curves of AI and SNR with the increasing keV were drawn. The AI and SNR were compared at lower (70 keV), medium (100 keV) and high (130 keV) level between MAR and non-MAR images using the paired t-test, and the subjective scores were compared using Wilcoxon signed rank-sum test. Results:The AI values around pedicle screws (anterior, posterior and lateral), 3D-printed VB and mesh cage decreased with the increase of keV, while SNR improved in MAR and non-MAR images. The AI values in the anterior, lateral and posterior pedicle screws and lateral titanium implants were significantly lower in MAR than those in non-MAR ( P<0.05). The AI value in posterior 3D-printed vertebral was lower in MAR than that of non-MAR only at 70 keV ( P<0.001). The SNR values in the anterior and posterior pedicle screws, 3D-printed VB increased with the increase of keV, but decreased in other ROIs. In the subjective evaluation, the image scores of MAR were higher than those of non-MAR ( P<0.05). Conclusion:Spectral CT using the MAR reconstruction can effectively reduce metal artifacts of spinal implants. The effect is better in pedicle screw and mesh cage than 3D-printed VB.
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Objective:To analyze the prognostic factors of primary and metastatic tumor resection for metastatic renal carcinoma.Methods:Clinical data of 12 cases of renal carcinoma with distant metastasis admitted to the Peking University Third Hospital from June 2011 to December 2019 were analyzed retrospectively, including 10 males and 2 females. Age was from 36 to 67 years old, with average of 53.7 years old. BMI was 20.9-30.8 kg/m 2, with average of 25.8 kg/m 2.There were 6 cases of right kidney tumor and 6 cases of left kidney tumor. The diameter of the primary tumor was 2.7-16.0 cm, with an average of 7.1 cm. There were 2 cases of lung metastasis, 1 case of liver metastasis and 9 cases of bone metastasis. All the 12 patients underwent primary and metastatic tumorectomy. Postoperative pathological results showed 10 cases of clear cell carcinoma, 1 case of papillary type 2 tumor and 1 case of collecting duct carcinoma. The pathological results of the metastases were the same as those of the original lesions. Results:All the 12 patients underwent primary and metastatic renal carcinoma resection, among which 3 received postoperative chemotherapy and 6 received radiotherapy .Two patients were treated with targeted drugs. The interval between primary resection and metastatic resection was 1-84 months, and the median time was 2.5 months. In this study, 12 patients were followed up for 2-96 months, with the median survival time of 34 months, and mortality rate of 25%.There was no significant correlation between age( P=0.265), gender( P=0.183), BMI( P=0.152), primary tumor size ( P=0.082), radiotherapy, chemotherapy or targeted therapy ( P=0.915) and overall survival, and the interval between primary resection and metastatic resection ( P=0.046) was significantly correlated with overall survival. Conclusion:The interval between primary and metastatic tumor resection was a risk factor for the prognosis of patients.
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The cross-fusion research of artificial intelligence technology and spinal surgery represented by machine learning and neural network model is a new research direction and hot issue in the field of artificial intelligence in recent years. The anatomy and disease symptoms of the spine are complex, and the diagnosis and treatment of spinal surgery require rich clinical experience. However, the distribution of medical resources in China is seriously uneven. How to improve the ability of primary medical services so that the most extensive patient groups can benefitis still an urgent problem to be solved. Artificial intelligence is a technical science that researches and develops theories, methods, technologies, and application systems for simulating, extending and expanding human intelligence. With the advent of the era of big data medical technology, artificial intelligence technology may solve this problem by transforming "experts sinking" into "tech sinking" . At present, technologies such as confrontation learning, weakly supervised learning, intensive learning and graph neural networks have become research hotspots in the field of artificial intelligence, and have also played an important role in many fields of clinical medicine. Based on the advantages of deep learning and neural network in disease learning, many spine surgeons combine it with the diagnosis and treatment of cervical spondylosis, low back pain, lumbar degenerative diseases, spinal deformity, spinal tumors, and other spine-related diseases. The rapid location and accurate diagnosis of the disease not only makes it an effective tool for the comprehensive diagnosis of spinal diseases but also provides the basis for the most reasonable treatment options for spinal diseases. In the domestic application of artificial intelligence in the diagnosis and treatment of spinal surgery, it can also solve the problems of difficult diagnosis and complicated treatment of spinal diseases faced by primary doctors, reduce the rate of misdiagnosis and missed diagnosis, and effectively reduce the economic and social burden of spinal diseases. This paper reviews the research progress of artificial intelligence represented by deep learning in the field of diagnosis and treatment of spinal surgery at home and abroad, and the advantages and application prospects of artificial intelligence in the diagnosis and treatment of spinal surgery.
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The cross-fusion research of artificial intelligence technology and spinal surgery represented by machine learning and neural network model is a new research direction and hot issue in the field of artificial intelligence in recent years.The anatomy and disease symptoms of the spine are complex,and the diagnosis and treatment of spinal surgery require rich clinical experience.However,the distribution of medical resources in China is seriously uneven.How to improve the ability of primary medical services so that the most extensive patient groups can benefitis still an urgent problem to be solved.Artificial intelligence is a technical science that researches and develops theories,methods,technologies,and application systems for simulating,extending and expanding human intelligence.With the advent of the era of big data medical technology,artificial intelligence technology may solve this problem by transforming "experts sinking" into "tech sinking".At present,technologies such as confrontation learning,weakly supervised learning,intensive learning and graph neural networks have become research hotspots in the field of artificial intelligence,and have also played an important role in many fields of clinical medicine.Based on the advantages of deep learning and neural network in disease learning,many spine surgeons combine it with the diagnosis and treatment of cervical spondylosis,low back pain,lumbar degenerative diseases,spinal deformity,spinal tumors,and other spine-related diseases.The rapid location and accurate diagnosis of the disease not only makes it an effective tool for the comprehensive diagnosis of spinal diseases but also provides the basis for the most reasonable treatment options for spinal diseases.In the domestic application of artificial intelligence in the diagnosis and treatment of spinal surgery,it can also solve the problems of difficult diagnosis and complicated treatment of spinal diseases faced by primary doctors,reduce the rate of misdiagnosis and missed diagnosis,and effectively reduce the economic and social burden of spinal diseases.This paper reviews the research progress of artificial intelligence represented by deep learning in the field of diagnosis and treatment of spinal surgery at home and abroad,and the advantages and application prospects of artificial intelligence in the diagnosis and treatment of spinal surgery.
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<p><b>BACKGROUND</b>Differentiated thyroid cancer (DTC) is a common primary cancer for spinal metastases (SM). The treatments for DTC spinal metastases (SM) have evolved from simple surgery and radiotherapy to a multidisciplinary comprehensive therapeutic strategy of combined spinal surgery, general surgery, radiotherapy, nuclear medicine and endocrinology. The purpose of this study was to discuss the efficacy and prognosis associated with different surgical treatments of SM patients with DTC.</p><p><b>METHODS</b>A total of 21 consecutive patients with SM of DTC that were treated between 1999 and 2013 were studied. Biopsy was routinely performed to achieve the pathological diagnosis before treatment. Three patients underwent total spondylectomy intralesionally or piecemeally, and 18 had curettage. Postoperative recurrence and survival times were analyzed by the Kaplan-Meier methods.</p><p><b>RESULTS</b>Nineteen patients (90%) had an average of 42.7 months (range, 7-170 months) follow-up. The median visual analogue scale for pain reduced from 5 points to 1 point (P < 0.01), and the median Karnofsky performance score increased from 70 to 90 points after surgery (P < 0.01). Seventeen patients with neurological deficits attained improvements after surgeries, of at least one level according to the Frankel classification (P < 0.01). Eight patients with curettage had recurrence. Four patients died of DTC, 12 patients lived with disease, and three patients were disease-free. No significant effects on postoperative recurrence or survival were observed between surgery combined with conservative treatment, total spondylectomy, the number of bone metastases and visceral metastasis.</p><p><b>CONCLUSIONS</b>DTC-SM have a relatively favorable prognosis, and curettage and stabilization can effectively relieve the pain and improve the quality of life and neurological status of the patients. For patients with Tomita scores of ≤3, total spondylectomy may have better clinical outcomes. Comprehensive therapeutic strategies including surgery, radioiodine, external beam radiation therapy and embolization should be considered for most patients.</p>